另外，通过局域波分析可把复杂的实测数据分解成有限个基本模式分量，从而简化信号分析过程，降低信号分析误差。
In addition, complicated tested data are decomposed into several intrinsic mode functions by this way, which low analyzing error, and predigest processing.
经验模式分解（EMD）通过筛分过程将原始信号分解成若干个基本模式分量（IMF），可看作无需预设带宽的自适应高通滤波方法。
Empirical mode decomposition(EMD) is a signal processing technique to decompose data set into several intrinsic mode functions(IMF) by a sifting process.
局域波分析方法的重大突破在于用基于信号局部特征的多个基本模式分量来描述信号，并赋予每个基本模式分量具有实际物理意义的瞬时频率。
The main innovations embodied in this method are the introduction of the intrinsic mode functions based on local properties of signals, which make the instantaneous frequency meaningful.
该文提出了一种新的非平稳信号的时变参数ARMA模型分析方法，用它分析数据需两个基本步骤：首先，用一种信号分解方法把信号分解成一些基本模式分量。
In this paper, a new method for time-varying ARM A model is introduced. It includes two procedures. First, using one method of signal decomposition, a signal is decomposed into some basic components;
利用局域波法将微弱的故障信号分解为有限的并且具有不同基本模式的分量，每个分量是单一成分信号，实现了信噪分离。
Weak fault signal was divided into finite local wave components with different simple-intrinsic modes, so that signal was separated from noise.
利用局域波法将微弱的故障信号分解为有限的并且具有不同基本模式的分量，每个分量是单一成分信号，实现了信噪分离。
Weak fault signal was divided into finite local wave components with different simple-intrinsic modes, so that signal was separated from noise.
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